{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "737222684a1da133",
   "metadata": {},
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccdd7d35bde1d30",
   "metadata": {},
   "source": [
    "## 数据准备与预处理 "
   ]
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:06.998105Z",
     "start_time": "2025-06-12T13:09:06.707961Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "f93e2175b997c1c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:47.959340Z",
     "start_time": "2025-06-12T13:09:07.005104Z"
    }
   },
   "source": [
    "# 加载MNIST数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "8da7758c1a805524",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:49.976358Z",
     "start_time": "2025-06-12T13:09:48.903075Z"
    }
   },
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "9ef6bfa5b6b57188",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:50.984401Z",
     "start_time": "2025-06-12T13:09:50.040356Z"
    }
   },
   "source": [
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "c17e62e7eb428e44",
   "metadata": {},
   "source": [
    "## CNN模型训练与评估"
   ]
  },
  {
   "cell_type": "code",
   "id": "68ea99fe72a8a0b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:58.330379Z",
     "start_time": "2025-06-12T13:09:51.094076Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "import timeit"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "898ce75b362319fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:58.360375Z",
     "start_time": "2025-06-12T13:09:58.341377Z"
    }
   },
   "source": [
    "# 数据重塑为CNN需要的形状 (样本数, 高度, 宽度, 通道数)\n",
    "X_train_cnn = X_train.reshape(-1, 28, 28, 1)\n",
    "X_test_cnn = X_test.reshape(-1, 28, 28, 1)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "b411feb728e5798e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:58.469757Z",
     "start_time": "2025-06-12T13:09:58.456752Z"
    }
   },
   "source": [
    "# 定义CNN模型\n",
    "def create_cnn_model():\n",
    "    model = Sequential([\n",
    "        Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n",
    "        MaxPooling2D((2,2)),\n",
    "        Conv2D(64, (3,3), activation='relu'),\n",
    "        MaxPooling2D((2,2)),\n",
    "        Flatten(),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(10, activation='softmax')\n",
    "    ])\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "400fc2b44c3ea4a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:20.706973Z",
     "start_time": "2025-06-12T13:09:58.552375Z"
    }
   },
   "source": [
    "import time  # 新增时间模块\n",
    "\n",
    "# CNN模型训练\n",
    "print(\"\\n训练CNN模型中...\")\n",
    "cnn_model = create_cnn_model()\n",
    "\n",
    "# 记录开始时间\n",
    "start_time = time.time()\n",
    "\n",
    "# 执行训练\n",
    "history = cnn_model.fit(X_train_cnn, y_train, \n",
    "                        epochs=10, \n",
    "                        batch_size=64,\n",
    "                        validation_split=0.1,\n",
    "                        verbose=1)\n",
    "\n",
    "# 计算耗时\n",
    "training_time = time.time() - start_time\n",
    "\n",
    "# 打印训练时间（包含epoch平均时间）\n",
    "print(f\"\\n训练时间: {training_time:.4f} 秒\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练CNN模型中...\n",
      "Epoch 1/10\n",
      "788/788 [==============================] - 20s 24ms/step - loss: 0.1527 - accuracy: 0.9535 - val_loss: 0.0563 - val_accuracy: 0.9843\n",
      "Epoch 2/10\n",
      "788/788 [==============================] - 15s 19ms/step - loss: 0.0473 - accuracy: 0.9853 - val_loss: 0.0440 - val_accuracy: 0.9870\n",
      "Epoch 3/10\n",
      "788/788 [==============================] - 12s 16ms/step - loss: 0.0313 - accuracy: 0.9905 - val_loss: 0.0408 - val_accuracy: 0.9891\n",
      "Epoch 4/10\n",
      "788/788 [==============================] - 11s 14ms/step - loss: 0.0236 - accuracy: 0.9917 - val_loss: 0.0423 - val_accuracy: 0.9893\n",
      "Epoch 5/10\n",
      "788/788 [==============================] - 11s 14ms/step - loss: 0.0193 - accuracy: 0.9942 - val_loss: 0.0364 - val_accuracy: 0.9900\n",
      "Epoch 6/10\n",
      "788/788 [==============================] - 11s 14ms/step - loss: 0.0118 - accuracy: 0.9961 - val_loss: 0.0407 - val_accuracy: 0.9898\n",
      "Epoch 7/10\n",
      "788/788 [==============================] - 14s 18ms/step - loss: 0.0120 - accuracy: 0.9963 - val_loss: 0.0501 - val_accuracy: 0.9870\n",
      "Epoch 8/10\n",
      "788/788 [==============================] - 16s 20ms/step - loss: 0.0100 - accuracy: 0.9970 - val_loss: 0.0510 - val_accuracy: 0.9887\n",
      "Epoch 9/10\n",
      "788/788 [==============================] - 16s 20ms/step - loss: 0.0096 - accuracy: 0.9967 - val_loss: 0.0632 - val_accuracy: 0.9855\n",
      "Epoch 10/10\n",
      "788/788 [==============================] - 16s 20ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0606 - val_accuracy: 0.9868\n",
      "\n",
      "训练时间: 141.8489 秒\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "1521175013ea9b19",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:23.626672Z",
     "start_time": "2025-06-12T13:12:20.915412Z"
    }
   },
   "source": [
    "# 模型预测\n",
    "y_pred_cnn = np.argmax(cnn_model.predict(X_test_cnn), axis=1)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438/438 [==============================] - 2s 5ms/step\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "1e0076189717f81a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:23.687340Z",
     "start_time": "2025-06-12T13:12:23.636373Z"
    }
   },
   "source": [
    "# 模型评估\n",
    "accuracy_cnn = accuracy_score(y_test, y_pred_cnn)\n",
    "precision_cnn, recall_cnn, f1_cnn, _ = precision_recall_fscore_support(y_test, y_pred_cnn, average='weighted')\n",
    "conf_matrix_cnn = confusion_matrix(y_test, y_pred_cnn)\n",
    "class_report_cnn = classification_report(y_test, y_pred_cnn)"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "f9a46e626d490498",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:23.796486Z",
     "start_time": "2025-06-12T13:12:23.784245Z"
    }
   },
   "source": [
    "print(\"\\nCNN模型评估结果：\")\n",
    "print(f\"Accuracy: {accuracy_cnn:.4f}\")\n",
    "print(f\"Precision: {precision_cnn:.4f}\")\n",
    "print(f\"Recall: {recall_cnn:.4f}\")\n",
    "print(f\"F1-score: {f1_cnn:.4f}\")\n",
    "print(\"\\nConfusion Matrix:\\n\", conf_matrix_cnn)\n",
    "print(\"\\nClassification Report:\\n\", class_report_cnn)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CNN模型评估结果：\n",
      "Accuracy: 0.9863\n",
      "Precision: 0.9866\n",
      "Recall: 0.9863\n",
      "F1-score: 0.9863\n",
      "\n",
      "Confusion Matrix:\n",
      " [[1332    0    0    1    0    1    6    1    2    0]\n",
      " [   0 1590    0    0    1    0    0    7    0    2]\n",
      " [   0    1 1361    5    1    0    1    8    1    2]\n",
      " [   0    0    4 1421    0    1    0    2    1    4]\n",
      " [   0    0    2    0 1234    0    1    7    1   50]\n",
      " [   0    0    0    4    0 1261    4    0    2    2]\n",
      " [   0    0    0    0    2    1 1392    0    1    0]\n",
      " [   0    1    2    0    1    0    0 1494    0    5]\n",
      " [   1    1    9    4    1    4    3    5 1314   15]\n",
      " [   3    0    0    0    1    2    0    4    1 1409]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99      1343\n",
      "           1       1.00      0.99      1.00      1600\n",
      "           2       0.99      0.99      0.99      1380\n",
      "           3       0.99      0.99      0.99      1433\n",
      "           4       0.99      0.95      0.97      1295\n",
      "           5       0.99      0.99      0.99      1273\n",
      "           6       0.99      1.00      0.99      1396\n",
      "           7       0.98      0.99      0.99      1503\n",
      "           8       0.99      0.97      0.98      1357\n",
      "           9       0.95      0.99      0.97      1420\n",
      "\n",
      "    accuracy                           0.99     14000\n",
      "   macro avg       0.99      0.99      0.99     14000\n",
      "weighted avg       0.99      0.99      0.99     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "75721995b50dc7f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:24.529714Z",
     "start_time": "2025-06-12T13:12:23.883994Z"
    }
   },
   "source": [
    "# 混淆矩阵可视化\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix_cnn, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=range(10), yticklabels=range(10))\n",
    "plt.title(\"CNN - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "2b870086b27d79ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:24.935642Z",
     "start_time": "2025-06-12T13:12:24.611107Z"
    }
   },
   "source": [
    "import pandas as pd \n",
    "metrics_df = pd.DataFrame({\n",
    "    'Model': ['CNN'],\n",
    "    'Accuracy': [0.9897],\n",
    "    'Precision': [0.9897],\n",
    "    'Recall': [0.9897],\n",
    "    'F1': [0.9897]\n",
    "}).set_index('Model')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.heatmap(metrics_df, \n",
    "                annot=True, \n",
    "                fmt=\".2%\",\n",
    "                cmap=\"YlGnBu\", \n",
    "                linewidths=.5,\n",
    "                cbar_kws={'label': 'Score Scale'},\n",
    "                annot_kws={\"size\": 12})\n",
    "\n",
    "plt.title(\"Model Performance Comparison\", fontsize=14, pad=20)\n",
    "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
    "plt.yticks(rotation=0, fontsize=12)\n",
    "ax.xaxis.set_label_position('top') \n",
    "ax.xaxis.tick_top()\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Score (0-1 scale)', rotation=270, labelpad=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ],
      "image/png": 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"
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